System and method for characterizing cardiac arrhythmia

ABSTRACT

In an embodiment, a data processing method comprises obtaining one or more photoplethysmography (PPG) signals from one or more PPG sensors of a monitoring apparatus, the PPG signals being generated based upon optically detecting pulsed variations in blood flow; obtaining a motion sensor signal from a motion sensor in the monitoring apparatus; identifying, based upon the motion sensor signal, one or more periods of motion (e.g., low motion) of the monitoring apparatus; and selectively obtaining and storing segments of the PPG signals based on a temporal relationship between the segments of the PPG signals and the identified periods of motion.

CROSS-REFERENCE TO RELATED APPLICATIONS; BENEFIT CLAIM

This application claims the benefit of Provisional Application No.62/257,600, filed Nov. 19, 2015, the entire contents of which are herebyincorporated by reference as if fully set forth herein, under 35 U.S.C.§ 119(e).

FIELD OF THE DISCLOSURE

The present disclosure generally relates to computer-implementedtechniques for characterizing cardiac arrhythmias. The disclosurerelates more specifically to programmed activity monitoring devices,computer programs, algorithms and methods that can be used tocharacterize cardiac arrhythmias.

BACKGROUND

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection.

In humans, an arrhythmia is an anomaly in heart function in whichheartbeats do not conform to normal sinus rhythm and thus beatabnormally. Typically, detecting arrhythmia requires using sensorapparatus that is uncomfortable or inconvenient for the patient to useor wear. For example, measurement of heart function by electrocardiogram(ECG or EKG) requires applying electrical leads to the patient's torso,typically with a sticky adhesive and a conductive gel. In some cases aportable apparatus such as the Holter monitor may be used. Theseapproaches create inconvenience to the patient as their movement may berestricted, or they may be unable to shower or bathe during the periodof wearing the electrodes.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1A illustrates general data flow and data processing according toan embodiment.

FIG. 1B illustrates an example process of selectively storing segmentsof PPG signals.

FIG. 1C illustrates an example process of dynamically changing thetemporal frequency at which PPG data is obtained in response todetecting an arrhythmia event.

FIG. 1D illustrates an example method of providing PPG signal data orPPG waveform data using an API.

FIG. 1E illustrates an example method of treatment of the human body.

FIG. 1F illustrates an example process of acquiring an electrocardiogrammeasurement in response to detecting an arrhythmia event.

FIG. 2 is a graph of an example cardiac arrhythmia caused by prematureatrial contractions.

FIG. 3 illustrates two graphs providing an example of data analysis thatmay be used to detect premature atrial contraction.

FIG. 4 illustrates a graph of an RR interval series associated withatrial fibrillation when a person converts from normal sinus rhythm toAF.

FIG. 5 illustrates data flow and processing steps in one embodiment thatis configured to characterize atrial fibrillation.

FIG. 6 illustrates data flow and processing steps in one embodiment thatis configured to characterize atrial fibrillation in which correlatedsympathetic or parasympathetic activity is considered.

FIG. 7 illustrates a computer system in which an activity monitoringdevice is integrated with a mobile computing device.

FIG. 8 illustrates an example dataset collected over approximately 25seconds, in which each heart beat produces a distinctive peak.

FIG. 9 is a block diagram that illustrates a computer system upon whichan embodiment of the invention may be implemented.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however,that the present invention may be practiced without these specificdetails. In other instances, well-known structures and devices are shownin block diagram form in order to avoid unnecessarily obscuring thepresent invention.

Embodiments are described in sections below according to the followingoutline:

1. General Overview

2. Example Implementation for Detecting Atrial Fibrillation

-   -   2.1 The Root Mean Square of Successive Differences (“RMSSD”)        Approach    -   2.2 Assessment of Correlated Sympathetic or Parasympathetic        Activity    -   2.3 PPG Waveform Shape Analysis    -   2.4 Integration with Mobile Computing and Cloud-Based Devices

3. Characterization of Other Arrhythmias

4. Benefits of Certain Embodiments

5. Hardware Overview

-   -   5.1 Activity Monitoring Device    -   5.2 Host Computer

6. Extensions and Alternatives

1. General Overview

In an embodiment, a data processing method comprises obtaining one ormore photoplethysmography (PPG) signals from one or more PPG sensors ofa monitoring apparatus, the PPG signals being generated based uponoptically detecting pulsed variations in blood flow; obtaining a motionsensor signal from a motion sensor in the monitoring apparatus;identifying, based upon the motion sensor signal, one or more periods ofmotion of the monitoring apparatus; selectively obtaining and storingsegments of the PPG signals based on a temporal relationship between thesegments of the PPG signals and the identified periods of motion.

According to one feature, the method may further comprise generating andstoring, based upon the PPG signals, PPG waveform data identifyingwaveform characteristics associated with the PPG signals; and based uponthe PPG waveform data and the one or more periods of motion, generatingand storing, based on PPG waveform data, arrhythmia event datadescribing one or more arrhythmia events.

In another aspect, the disclosure provides a data processing method thatcomprises obtaining, at a server computer, one or more ofphotoplethysmography (PPG) signals or PPG waveform data identifyingwaveform characteristics associated with the PPG signals from amonitoring apparatus via a wireless communication link, the PPG signalsand the PPG waveform data not including PPG data associated withdetected periods of motion of the monitoring apparatus; obtaining, atthe server computer, a call to an application programming interface(API) of the server computer that requests one or more of the PPGsignals or the PPG waveform data; and in response to the call,transmitting, from the server computer to a host computer thattransmitted the call, the requested PPG signals or PPG waveform data.

In a further aspect, an activity monitoring device comprises one or moreprocessors coupled to electronic digital memory; in the memory, one ormore sequences of programmed instructions which when executed by the oneor more processors cause the one or more processors to perform:obtaining one or more photoplethysmography (PPG) signals from one ormore PPG sensors of a monitoring apparatus, the PPG signals beinggenerated based upon optically detecting pulsed variations in bloodflow; obtaining a motion sensor signal from a motion sensor in themonitoring apparatus; identifying, based upon the motion sensor signal,one or more periods of motion of the monitoring apparatus; andselectively obtaining and storing segments of the PPG signals based on atemporal relationship between the segments of the PPG signals and theidentified periods of motion.

In yet another aspect, a method of treatment of the human body comprisesobtaining one or more photoplethysmography (PPG) signals from one ormore PPG sensors of a monitoring apparatus, the PPG signals beinggenerated based upon optically detecting pulsed variations in bloodflow; obtaining a motion sensor signal from a motion sensor in themonitoring apparatus; identifying, based upon the motion sensor signal,one or more periods of motion of the monitoring apparatus; selectivelyobtaining and storing segments of the PPG signals based on a temporalrelationship between the segments of the PPG signals and the identifiedperiods of motion; generating and storing, based on the segments of thePPG signals, PPG waveform data identifying waveform characteristicsassociated with the PPG signals; generating and storing, based on PPGwaveform data, arrhythmia event data describing one or more arrhythmiaevents; and based on the arrhythmia event data describing the one ormore arrhythmia events, generating and transmitting to a user associatedwith the monitoring apparatus, an electronic digital recommendationmessage comprising a recommendation for treatment of the one or morearrhythmia events.

In still another aspect, a data processing method comprises obtainingone or more photoplethysmography (PPG) signals from one or more PPGsensors of a monitoring apparatus at a first temporal resolution, thePPG signals being generated based upon optically detecting pulsedvariations in blood flow; generating and storing, based upon the PPGsignals, a PPG waveform dataset identifying waveform characteristicsassociated with the PPG signals; detecting, based upon the PPG waveformdataset, one or more cardiac arrhythmia events; and in response todetecting, based on the PPG waveform dataset, the one or more cardiacarrhythmia events, modifying a temporal resolution configurationparameter of the monitoring apparatus to cause the PPG signals to bereceived at a second temporal resolution that is higher than the firsttemporal resolution.

In one embodiment, computer-implemented techniques can identify cardiacarrhythmias in humans based upon processing data representing the rhythmof the heart that has been obtained by monitoring the pulsephotoplethysmogram (PPG) of the blood flow arising from the heart.

In one embodiment, one or more PPG sensors in a personal activitymonitoring device are used to acquire PPG signals representing heartbeatand/or heart rhythm. In various embodiments, the activity monitoringdevice may be configured or adapted to be worn on the wrist, fingertip,ear, or forehead. In general embodiments may be configured to be worn orattached to any surface of the body having characteristics of the skinand blood vessels that permit an external, non-invasive apparatus tooptically detect PPG signals representative of blood flow.

As further described herein, the particular hardware elements used toimplement different embodiments may vary, but in one embodiment, anactivity monitoring device comprises at least one motion sensor, one ormore optical sensors, a digital processor such as a microcontroller orCPU, and digital memory storing instructions implemented as software orfirmware that are programmed to determine intervals of heart rhythmbased, for example, upon determining time differences between successivesignal peaks represented in the acquired signals or data.

In some embodiments, a signal from the motion sensor is used to detectperiods of sleep or low motion, and perform analysis of pulse intervaldata to detect arrhythmia only during those periods. These embodimentsrecognize that greater blood-oxygen perfusion may occur when the body iswarmer during sleep and/or as a result of vasodilation, and that thebody at rest produces a more stable PPG signal. Detecting sleeptherefore may result in using a better PPG signal and thus betterassessment of arrhythmia conditions.

Still other embodiments may use the motion sensor signal to detectperiods of exercise or activity, and in response, to trigger use ofdetection algorithms. In some embodiments, the detection algorithms mayimplement PPG data collection at a higher frequency for a time periodoccurring just after the time period of exercise or activity. In thisembodiment, computer-based analysis of the rate of arrhythmias in theperiod of rest soon after an exercise will provide information similarto that obtained with a clinical stress test. The motion sensor can beused to infer the periods of exercise and stillness for which thismetric should be measured.

Chronotropic incompetence can be observed by measuring the rate ofincrease of heart rate in response to physical activity measured with amotion sensor. In one particular embodiment, the motion sensor signal isused to detect periods of exercise or activity, and to triggermeasurement of heartbeat as well as changes that could indicatechronotropic incompetence. In yet another embodiment, the system may beprogrammed to automatically detect the conditions of a cardiac clinicalstress test, which typically involve a period of exercise followed bylow motion. During the period of low motion soon after exercise, PPGwaveform data may be characterized with respect to any of thearrhythmias that are otherwise disclosed herein.

In an embodiment, techniques are provided to detect and discarderroneous pulses in the PPG data.

In another embodiment, low amplitude PPG signals are detected andamplitude values are used to infer arrhythmia.

Embodiments may be programmed or configured to detect several differentforms of arrhythmia. For example, embodiments may be programmed orconfigured to perform any one or more of the following:

-   -   Detecting premature atrial contraction using a running average        formula    -   Detecting bradycardia over a timescale that is consistent with        sleep apnea    -   Detecting tachycardia over a timescale that is consistent with        sleep apnea    -   Detecting atrial fibrillation (“AF”) and record the amount and        duration of the fibrillation (commonly referred to as the “AF        burden”)    -   Detecting a drop in heart rate during sleep    -   Estimating heart rate variability (HRV)

Embodiments also may be programmed or configured to perform, in responseto detecting any of the foregoing, increasing the frequency ofcollecting PPG data using the monitoring apparatus, and/or increasingthe frequency of uploading the collected data to another computer, suchas a networked or cloud-based server computer, and/or generating one ormore alert signals, messages or notifications as output to themonitoring apparatus, another computer, or another device.

Embodiments may include one or more host computers that are coupled bynetwork connections to the monitoring apparatus. A particular hostcomputer may be programmed or configured as a server to a plurality ofmonitoring devices having the role of clients of the server. The hostcomputer may implement a variety of data characterizing, analysis and/orreporting processes. For example, in one embodiment, a host computer isprogrammed or configured to request (as by polling), receive (as bypushing), or otherwise obtain periodic updates from the activitymonitoring apparatus. Updates may comprise raw PPG signal data collectedby the activity monitoring apparatus and/or heartbeat or heart rhythmdata or signals that have been formed by the activity monitoringapparatus.

The host computer may be programmed to perform validation or furtheranalysis of any of the physiological conditions listed above, takingadvantage of the greater processing power that is typically available ata host computer or server computer as compared to a miniature activitymonitoring apparatus that is worn on the body. The host computer alsomay be programmed or configured to output a variety of metrics includingbut not limited to heartbeats, sequences, number of abnormal sequences,and other metrics.

Therefore, embodiments herein provide a non-invasive means of monitoringcardiac arrhythmias (such as frequent premature atrial contractions orparoxysmal atrial fibrillation) which provide convenience to bothpatients and any clinical personnel involved in the care of suchpatients.

FIG. 1A illustrates general data flow and data processing according toan embodiment. In one example embodiment, an activity monitoring device101 is worn on the body (e.g., during a period of sleep or activity).The activity monitoring device 101 may be implemented using apparatushaving a variety of form factors. In various embodiments, the device 101comprises a wrist-mounted device, such as the FitBit Charge HRcommercially available from Fitbit, Inc., San Francisco, Calif., or adevice worn on another body part such as the fingertip, ear, or head.The apparatus such as activity monitoring device 101 comprises at leastone motion sensor 122 which measures movement of the body, one or moreoptical sensors 120 that are configured to measure pulsatile variationsin the blood flow in the body (e.g., a portion of the body adjacent tothe device 101 when the device 101 is worn), and a control program 124that implements functions and processes that are further describedherein.

The one or more optical sensors 120 generate, as output, aphotoplethysmogram (PPG) signal 103. The PPG signal 103 can be acquired,in various embodiments, using green, red or infrared light sources, orwith one or more types of light sources simultaneously. The opticalsensors 120 may comprise one or more such light sources positionedproximate to a detector. Examples of light sources includelight-emitting diodes (LEDs), filament-based lamps, phosphor lightsources, and lasers. Examples of detectors include photodiodes,phototransistors, CCD sensors, thermopile detectors, and CMOS camerasensors.

The light emitted by a light source includes wavelength and frequencyproperties, which are inversely related to one another. Differentwavelengths may be more beneficial for the acquisition of a PPG signalunder certain operating conditions of the activity monitoring device101. Operating conditions may include skin pigmentation level, ambienttemperature, and/or surface temperature of the skin. For example, thepenetration depth of light is dependent on its wavelength. A longerwavelength can penetrate to deeper tissue than a shorter wavelength.Thus, in some instances, based on pigmentation level of the user, it maybe preferable to use a light source with a longer wavelength, in orderto acquire an accurate PPG signal. In another example, a shorterwavelength may be preferable for acquiring an accurate PPG signal whenthe ambient temperature and/or surface temperature of the skin iscooler.

Light sources of various wavelengths can be emitted by a light source,including, but not limited to 560 nm, 640 nm, and/or 960 nm. In oneembodiment, the wavelength or frequency of the light emitted by thelight source may be modified, adjusted, and/or controlled to improve theacquisition of the PPG signal under given operating conditions.

The PPG signal 103 typically comprises signal variations that areassociated with each heart beat as blood volume flows through thearteries in the body. As seen at block 105, in an embodiment, theactivity monitoring device 101 or a host computer 110 is programmed orconfigured to identify waveform characteristics based upon the PPGsignal. In various embodiments, block 105 may represent programming toextract information about the cardiac rhythm such as peak-to-peakintervals (“PP intervals” or “RR intervals”) represented in the PPGsignal 130, to compute an area under a curve represented in graph orwaveform of the PPG signal 130, or other techniques. Because either theactivity monitoring device 101 or a host computer 110 may be programmedor configured to obtain such data and perform other processing, theactivity monitoring device and host computer may be termed “the system”for purposes of this disclosure as each constitutes a useful dataprocessing system for the purposes described herein. In an embodiment,time between successive heart beats is measured in seconds and comprisesthe time between successive peaks of the PPG signal. Various embodimentsmay use different techniques to extract the RR intervals. For example,the system may find peaks in the PPG signal 130 using template matching.

In various embodiments, the motion sensor signal 102 comprises any of anaccelerometer sensor signal from an accelerometer, an altitude signalfrom an altimeter, a gyroscopic motion signal from a gyroscope, or ageographical position signal from a global positioning system (GPS)receiver. In various embodiments, one or more motion-related metrics maybe generated based upon motion sensor signal 102. In one embodiment, amotion-related metric may be data or a value derived or generated basedon a motion sensor signal 102, such as a standard of deviation of themotion sensor signal 102. In another embodiment, a motion-related metricof a motion sensor signal 102 is the motion sensor signal 102 itself. Insome embodiments, a motion sensor signal 102 may refer to the motionsensor signal data received from motion sensor 122, and in otherembodiments, a motion sensor signal 102 may refer to a motion-relatedmetric generated based on motion sensor signal data received from motionsensor 122. In one embodiment, motion sensor 122 comprises a tri-axialaccelerometer.

Embodiments recognize that the presence of motion by the body can causeobtaining a poor quality PPG signal 103. In an embodiment, to minimizethe possibility of misdetection of PP intervals, a motion sensor signal102 obtained from motion sensor 122 is used to detect and processperiods of motion, as shown at block 107. In various embodiments,processing at block 107 may include steps to mask out or exclude periodsof high movement, or to identify periods of sleep for purposes ofanalysis of conditions such as arrhythmias or sleep apnea during theperiods of sleep. The motion sensor signal 102 represents periods ofactivity of the body as measured by the motion sensor 122. The motionsensor 122 also may be used to infer periods of sleep and to cause theactivity monitoring device 101 or host computer 110 to search the datafor datasets indicating arrhythmias only during periods of sleep orduring particular stages of sleep.

In some embodiments, the activity monitoring device 101 may include aglobal positioning system (GPS) receiver and a GPS signal from thereceiver may be used additionally or as an alternative to the use of themotion sensor signal 102 for detecting motion of the activity monitoringdevice. Thus, in all instances in this disclosure in which use of themotion sensor 122 or the motion sensor signal 102 are described, a GPSsignal could be used as a supplement or a substitute.

Further, embodiments may be programmed or configured to adaptivelyremove artifacts from the PPG data in response to motion sensor inputand/or an expected error rate in the data arising from rotation ormovement of the activity monitoring device 101. As one example, adaptivedetection of artifacts may comprise continuously comparing windows ofPPG data to a template, and in response to detecting aberrations,removing artifacts in the PPG data to more closely match the template.By using the motion sensor signal 102 as a trigger, this approachprevents removing artifacts too aggressively by presuming that the PPGdata is correct (and thus will not be removed) unless both the motionsensor signal 102 indicates motion and a poor match to a template isfound. Additionally or alternatively, the control program 124 mayimplement filtering processes to filter out low-frequency noise in thePPG data, where such noise may be caused by breathing.

Based upon the PP intervals that have been determined at block 105, andexcluding periods of motion as seen at block 107, in an embodiment, theresulting data may be used to classify the PPG data as indicating one ormore heart rhythm types, as seen at block 109.

FIG. 1B illustrates an example process of selectively storing segmentsof PPG signals. In one embodiment, FIG. 1B may serve as an algorithmthat may be used as a basis for programming instructions for controlprogram 124. Block 130 comprises obtaining one or morephotoplethysmography (PPG) signals from one or more PPG sensors of amonitoring apparatus, the PPG signals being generated based uponoptically detecting pulsed variations in blood flow. Block 132 comprisesobtaining a motion sensor signal from a motion sensor in the monitoringapparatus. Block 134 comprises identifying, based upon the motion sensorsignal, one or more periods of motion of the monitoring apparatus. Block136 comprises selectively obtaining and storing segments of the PPGsignals based on a temporal relationship between the segments of the PPGsignals and the identified periods of motion.

In an embodiment, the temporal relationship indicates that theselectively obtained and stored segments of the PPG signals do notcorrespond to the identified periods of motion. In an embodiment,control program 124 may be programmed to cause generating and storing,based upon the segments of the PPG signals, PPG waveform dataidentifying waveform characteristics associated with the PPG signals,and generating and storing, based on the PPG waveform data, arrhythmiaevent data describing one or more arrhythmia events. The PPG waveformdata can include a peak-to-peak dataset comprising one or more intervalvalues representing time intervals between one or more successive peaksin the PPG signals. Additionally or alternatively, the PPG waveform dataincludes an amplitude dataset comprising one or more amplitude valuesassociated with one or more peaks in the PPG signals. The arrhythmiaevent data may describe one or more arrhythmia types and arrhythmiadurations that are associated with the one or more arrhythmia types.

In one approach, the system is programmed to cause comparing amotion-related metric generated based upon the motion sensor signal to aspecified motion threshold value; determining, based on the comparing,that the one or more periods of motion indicate low motion; anddetermining and storing one or more arrhythmia types and arrhythmiadurations that are associated with the one or more arrhythmia types onlyduring the one or more periods of motion that indicate low motion. Thesystem also may be programmed to cause comparing a motion-related metricgenerated based upon the motion sensor signal to a specified motionthreshold value; determining, based on the comparing, that the one ormore periods of motion indicate sleep, and determining and storing oneor more arrhythmia types and arrhythmia durations that are associatedwith the one or more arrhythmia types only during the one or moreperiods of motion that indicate sleep. Or, the system may be programmedto cause comparing a motion-related metric generated based upon themotion sensor signal to a specified motion threshold value; determining,based on the comparing, that the one or more periods of motion indicatehigh motion; and in response thereto, determining and storing one ormore arrhythmia types and arrhythmia durations that are associated withthe one or more arrhythmia types only during additional time periodsthat do not correspond to the one or more periods of motion thatindicate high motion.

As one example of arrhythmia detection, the system may be programmed fordetecting one or more low amplitude PPG signals in the PPG signals, andin response thereto, classifying a portion of the PPG signalscorresponding to the low amplitude PPG signals as representing anarrhythmia.

A feedback loop from block 109 to other functions or routines of thecontrol program 124 may be implemented for purposes of changingoperation of the control program in response to the characteristics thathave been detected. For example, feedback specifying a classification orcharacterization of the PPG data as arrhythmia, from block 109, maycause the control program to begin transmitting data to host computer110 at higher temporal resolution or frequency when the control program124 detects signs of AF or arrhythmias.

Alternatively, the control program 124 may be configured for comparing amotion-related metric generated based upon the motion sensor signal to aspecified motion threshold value; determining, based upon the comparing,that the one or more periods of motion indicate exercise, and inresponse thereto, determining and storing one or more arrhythmia typesand arrhythmia durations that are associated with the one or morearrhythmia types only during additional time periods that do notcorrespond to the one or more periods of motion that indicate exercise.One feature of this approach may include, before the determining thatthe one or more periods of motion indicate exercise, obtaining the PPGsignals at a first temporal resolution, and determining, based upon themotion sensor signal, that the one or more periods of motion indicatingexercise have ended and are followed by one or more periods of lowmotion, and in response thereto, modifying a temporal resolutionconfiguration parameter of the monitoring apparatus to cause the PPGsignals associated with the one or more periods of low motion to beobtained at a second temporal resolution that is higher than the firsttemporal resolution. The control program 124 also could be configuredfor classifying, based on the obtained PPG signals associated with theone or more periods of low motion, a portion of the PPG signals asrepresenting success or failure in a clinical stress test. Or, thecontrol program 124 may be configured to perform determining, based uponthe motion sensor signal, that the one or more periods of motionindicating exercise have ended and are followed by one or more periodsof low motion, and in response thereto, determining and storing one ormore arrhythmia types and arrhythmia durations that are associated withthe one or more arrhythmia types during the one or more periods of lowmotion that follow the one or more periods of motion indicatingexercise. If the system detects one or more arrhythmias during the oneor more periods of low motion that follow the one or more periods ofmotion indicating exercise, the system may classify a correspondingportion of the PPG signals (associated with the one or more periods oflow motion) as a failure of a clinical stress test.

As another example, the control program 124 may be configured forcomparing a motion-related metric generated based upon the motion sensorsignal to a specified motion threshold value; determining, based uponthe comparing, that the one or more periods of motion indicate exercise,and in response thereto, identifying an instance of chronotropicincompetence, based on one or more heart rate values measured during theone or more periods of motion that indicate exercise.

FIG. 1C illustrates an example process of dynamically changing thetemporal frequency at which PPG data is obtained in response todetecting an arrhythmia event. In one embodiment, FIG. 1C may serve asan algorithm that may be used as a basis for programming instructionsfor control program 124. In one embodiment, a method or algorithmcomprises obtaining one or more photoplethysmography (PPG) signals fromone or more PPG sensors of a monitoring apparatus at a first temporalresolution, the PPG signals being generated based upon opticallydetecting pulsed variations in blood flow, as seen at block 140. Atblock 142, the process is configured for generating and storing, basedupon the PPG signals, a PPG waveform dataset identifying waveformcharacteristics associated with the PPG signals. At block 144, theprocess is configured for detecting, based upon the PPG waveformdataset, one or more cardiac arrhythmia events. At block 146, inresponse to detecting, based on the PPG waveform dataset, the one ormore cardiac arrhythmia events, the process modifies a temporalresolution configuration parameter of the monitoring apparatus to causethe PPG signals to be received at a second temporal resolution that ishigher than the first temporal resolution.

In such an embodiment, the cardiac arrhythmia events comprise any one ormore of cardiac chronotropic incompetence, tachycardia, bradycardia,atrial fibrillation or other cardiac arrhythmia. The approach mayfurther comprise, based upon the PPG waveform dataset, determining andstoring one or more cardiac arrhythmia datasets representing, for eachof the cardiac arrhythmia events, a type and a duration associated withthe cardiac arrhythmia event. In yet another feature of the approach ofFIG. 1C, the method may comprise generating, based upon the PPG signalsof the second temporal resolution, a second PPG waveform datasetidentifying additional waveform characteristics associated with the PPGsignals of the second temporal resolution; and based upon the second PPGwaveform dataset, determining and storing one or more high resolutioncardiac arrhythmia datasets representing one or more cardiac arrhythmiatypes and durations associated with the types. In a related feature ofthe approach of FIG. 1C, the method may comprise periodicallytransferring one or more of the PPG signals of the second temporalresolution, the second PPG waveform dataset or the one or more highresolution cardiac arrhythmia datasets from the monitoring apparatus toa server computer using a wireless communication link; receiving, at theserver computer, a call to an application programming interface (API) ofthe server computer that requests one or more of the PPG signals of thesecond temporal resolution, the second PPG waveform dataset or the oneor more high resolution cardiac arrhythmia datasets; and in response tothe call, transmitting, from the server computer to a host computer thattransmitted the call, one or more of the PPG signals of the secondtemporal resolution, the second PPG waveform dataset or the one or morehigh resolution cardiac arrhythmia datasets.

In a further feature of the approach of FIG. 1C, the method may compriseusing the server computer, generating one or more output metrics basedupon one or more of the PPG signals of the second temporal resolution,the second PPG waveform dataset or the one or more high resolutioncardiac arrhythmia datasets; receiving, at the server computer, a callto an application programming interface (API) of the server computerthat requests one or more of the output metrics; and in response to thecall, transmitting, from the server computer to a host computer thattransmitted the call, one or more of the output metrics.

Any of these approaches relating to the method of FIG. 1C may furthercomprise receiving a motion sensor signal from a motion sensor in themonitoring apparatus; identifying, based upon the motion sensor signal,one or more periods of motion of the monitoring apparatus; and excludingcardiac data associated with the periods of motion from the PPG signalsof the second temporal resolution, the second PPG waveform dataset, orthe one or more high resolution cardiac arrhythmia datasets. The processmay be programmed for generating, as the output metrics, one or moredata values representing heartbeats, heartbeat sequences, number ofabnormal heartbeat sequences, number of arrhythmia events, or type ofarrhythmia events. The approach may further comprise determining, basedon the PPG waveform dataset, that the one or more cardiac arrhythmiaevents have concluded; and modifying the temporal resolutionconfiguration parameter of the monitoring apparatus to cause the PPGsignals to be received at the first temporal resolution.

An example of a standard frequency of data collection is 25 Hz, and anexample of higher resolution is 100 Hz, but other embodiments may useother frequencies. Higher temporal resolution of PPG can allow theextraction of high fidelity morphological features which can then leadto more accurate classification of PPG segments that constitutearrhythmic event candidates. Detection of an elevated heart rate as acharacteristic at block 109 also could trigger a change in the frequencyof data collection under control of the control program 124. In oneembodiment, detection of an arrhythmia event at block 109 may cause achange in the amount of data collected, or both the frequency and therange. For example, for some arrhythmia events, capturing datasurrounding the event may be useful. To address this, the controlprogram 124 may implement algorithms, functions or routines fordetecting short-term arrhythmia events and, in response, storing ortransmitting PPG data that is obtained around such events. In oneembodiment, the control program 124 may be configured or programmed tocause continuously capturing the PPG data in a circular buffer; the sizeof such a buffer may vary in different embodiments and examples might bebuffers that capture 10 seconds of data, 60 seconds of data, etc. Insuch an embodiment, if the system detects a possible arrhythmia event ofinterest at a given time, the system could move the contents of thebuffer, representing PPG data obtained earlier than the event, tonon-volatile storage, to other separate storage, or to a server computerby uploading. Additionally or alternatively, the system could change thedata collection frequency in response to the event, so that the bufferis subsequently stored with high frequency data, and then upload,separately store, or otherwise process the high frequency data that wasobtained after the event. Further, the control program 124 may usefeedback from block 109 for tracking periods of sleep using a motionsensor to measure a drop in heart rate during sleep, or to triggerhigher frequency data collection for purposes of assessing sleep apnea,as further described herein.

To further increase the robustness of PP estimation, erroneous PP pulsescan be detected by calculating one or more morphological features fromthe pulse, such as maximum & minimum amplitude, maximum and minimumslope, area, or width, and comparing these features with the samefeatures obtained from neighboring pulses, or averaged values of thesame features obtained from neighboring pulses. If the comparison yieldslarge relative differences, then the pulse can be discarded.

Additionally or alternatively, with respect to characterizing rhythm atblock 109, other output metrics may be generated as seen at block 112.For example, during periods of sleep and stillness, block 112 cancomprise generating and outputting values for output metrics such asnumber of ventricular beats, number of two beat sequences (“bigeminy”),number of three beat sequences (“trigemini”), or others.

In various embodiments, all or part of blocks 105, 107, 109, 112 may beimplemented using software or firmware that is hosted by and executedusing the activity monitoring device 101 or the host computer 110. Forexample, the activity monitoring device 101 may be configured orprogrammed to obtain the motion sensor signal 102 and the PPG signal103, and to periodically upload data representing these signals via oneor more networks 108 to the host computer 110, and the host computerthen may perform one or more of blocks 105, 107, 109, 112 in the mannerdescribed herein. Uploading may occur continuously, at particular timesas specified in a schedule, or in response to specified events such asapproaching or reaching a maximum storage capacity of memory in theactivity monitoring device 101. Or, the activity monitoring device 101may perform one or more of blocks 105, 107, 109, 112, and may provide adisplay at the device of the output of block 109 or block 112 or both.Additionally or alternatively, the activity monitoring device mayperform one or more of blocks 105, 107, 109, 112, and may upload orotherwise provide output data resulting from block 109 or block 112 tothe host computer 110 for further analysis, reporting, logging or otheruse. Uploading may involve transferring data in the form as originallycollected at the activity monitoring device 101, or compressiontechniques may be used to compress the data prior to transfer.

In an embodiment, the host computer 110 may implement an applicationprogramming interface (API) to permit third party server computers orapplications to send API calls and to receive responses comprising anyof the data that the host computer has collected or formed using theprocesses described above.

FIG. 1D illustrates an example method of providing PPG signal data orPPG waveform data using an API. In an embodiment, FIG. 1D may serve asan algorithm that may be used as a basis for programming instructionsfor control program 124. In one embodiment, at block 150, the processperforms obtaining, at a server computer, one or more ofphotoplethysmography (PPG) signals or PPG waveform data identifyingwaveform characteristics associated with the PPG signals from amonitoring apparatus via a wireless communication link, the PPG signalsand the PPG waveform data not including PPG data associated withdetected periods of motion of the monitoring apparatus. At block 152,the process is configured for obtaining, at the server computer, a callto an application programming interface (API) of the server computerthat requests one or more of the PPG signals or the PPG waveform data.At block 154, the process is configured for transmitting from the servercomputer, in response to the call, to a host computer that transmittedthe call, the requested PPG signals or PPG waveform data.

One feature of this approach may include, using the server computer,generating and storing one or more output metrics based upon one or moreof the PPG signals or the PPG waveform data; obtaining, at the servercomputer, a call to the application programming interface (API) of theserver computer that requests one or more of the output metrics; and inresponse to the call, transmitting, from the server computer to a hostcomputer that transmitted the call, the requested output metrics. Theoutput metrics may comprise one or more data values representingheartbeats, heartbeat sequences, number of abnormal heartbeat sequences,number of arrhythmia events, or type of arrhythmia events.

In one embodiment, the detected periods of motion may be detected basedon a motion sensor signal from an accelerometer in the monitoringapparatus. Further, in an embodiment, the monitoring apparatus obtainsraw PPG data from one or more PPG sensors of the monitoring apparatus,and the monitoring apparatus selectively obtains, from the raw PPG data,the PPG signals not including PPG data associated with the detectedperiods of motion. For example, the activity monitoring device 101 maybe programmed to determine periods of motion represented in the raw PPGdata and to exclude periods of motion from the PPG signals that areuploaded to the host computer 110 and thereby become available via theAPI.

In other embodiments, various therapeutic methods may be performed basedon the data and analysis described above. For example, embodimentsencompass a method of providing a diagnosis of arrhythmia. Embodimentsalso encompass methods of recommending treatment using any of betablockers, calcium channel blockers, digoxin, sodium channel blockers,potassium channel blockers or blood thinners such as anti-platelets andanti-coagulants.

FIG. 1E illustrates an example method of treatment of the human body. Inone embodiment, at block 160, the method comprises obtaining one or morephotoplethysmography (PPG) signals from one or more PPG sensors of amonitoring apparatus, the PPG signals being generated based uponoptically detecting pulsed variations in blood flow. At block 162, theprocess comprises obtaining a motion sensor signal from a motion sensorin the monitoring apparatus. At block 164, the process comprisesidentifying, based upon the motion sensor signal, one or more periods ofmotion of the monitoring apparatus. At block 166, the process comprisesselectively obtaining and storing segments of the PPG signals based on atemporal relationship between the segments of the PPG signals and theidentified periods of motion. At block 168, the process comprisesgenerating and storing, based on the segments of the PPG signals, PPGwaveform data identifying waveform characteristics associated with thePPG signals. At block 170, the process comprises generating and storing,based on PPG waveform data, arrhythmia event data describing one or morearrhythmia events. At block 172, the process comprises, based on thearrhythmia event data describing the one or more arrhythmia events,generating and transmitting to a user associated with the monitoringapparatus, an electronic digital recommendation message comprising arecommendation for treatment of the one or more arrhythmia events.

Classifying rhythm types at block 109 may, in one embodiment, providedata useful as input in generating a treatment plan for a patient, asseen at block 114. In such an embodiment, block 114 may compriseperforming various therapeutic methods based on the data and analysisdescribed above. For example, embodiments encompass a method ofproviding a diagnosis of arrhythmia. Embodiments also encompass methodsof recommending treatment using any of beta blockers, calcium channelblockers, digoxin, sodium channel blockers, potassium channel blockersor blood thinners such as anti-platelets and anti-coagulants. When block109 indicates bradycardia, then the treatment plan formed at block 114may comprise implanting a pacemaker. When block 109 indicates AF, thetreatment plan created at block 114 may comprise treatment by medicationor a procedure to cardiovert the heart. In any of these embodiments,block 114 may comprise, based on arrhythmia event data generated atblock 109 and describing one or more arrhythmia events, generating andtransmitting to a user associated with the monitoring apparatus, anelectronic digital recommendation message comprising a recommendationfor treatment of the one or more arrhythmia events, and comprising anyone or more of the treatment plans or recommendations described above.

Embodiments recognize that information about cardiac rhythm also can bereflected in values of the amplitude of the PPG signal 103. For example,a premature atrial contraction may result in a less effective beat, sothat the volume of blood passing under the PPG sensor is reduced,thereby causing a lower amplitude signal. In an embodiment, the activitymonitoring device 101 or host computer 110 are configured or programmedto perform, as part of block 109, detection of low amplitude datasets ofthe PPG signal 103. In one embodiment, the activity monitoring device101 or host computer 110 is configured or programmed to compare eachpeak signal value detected in the PPG signal to a stored threshold valueindicating a minimum amplitude value that is associated with normalrhythm. If a particular peak is below the threshold value, then inresponse, the activity monitoring device 101 or host computer 110 maystore a record identifying a point at which a low amplitude peak valuewas detected. The record may include, for example, data identifying thePPG signal, a tag or type value signifying low amplitude, and atimestamp or sequence number indicating a position within the entire PPGsignal dataset at which the low amplitude peak is found.

In an embodiment, activity monitoring device 101 or host computer 110 isconfigured or programmed to detect premature ventricular contraction(PVC). For example, activity monitoring device 101 or host computer 110is configured or programmed to compute and store an area valuerepresenting the area under the PPG curve, and in response, to comparethe area value to a stored threshold area value associated with PVCand/or to stored area values associated with the preceding N heartbeats,where 1<=N<=10. Such embodiments recognize that a PVC may be manifest asone or more signals having higher amplitude and area compared to thelast two or three normal beats preceding the PVC.

In an embodiment, activity monitoring device 101 or host computer 110 isconfigured or programmed to detect periods of sleep apnea. In oneexample embodiment, the activity monitoring device 101 or host computer110 is configured or programmed to detect bradycardia or tachycardiapatterns at timescales that are consistent with apnea events. Forexample, in one embodiment, the activity monitoring device 101 or hostcomputer 110 is configured or programmed to inspect values of the PPGsignal 103 over a specified time period, such as 20 to 30 seconds, toidentify periods of bradycardia or tachycardia. Template matching may beused for this purpose.

In an embodiment, activity monitoring device 101 or host computer 110 isconfigured or programmed to detect other forms of arrhythmia, as furtherdescribed. For example, control program 124 may be configured to obtainPPG signals and a motion sensor signal as previously described and toperform, based upon the motion sensor signal, detecting a period of lowmotion of the apparatus consistent with sleep of a user over a specifiedtime sufficient to permit detection of sleep apnea; detecting, based onthe PPG signals, an indication of bradycardia; and in response thereto,marking a portion of the PPG signals corresponding to the period of lowmotion as representing bradycardia. A portion of the PPG signals may bemarked as representing sleep apnea. Further, the control program 124 maybe configured to perform, based upon the motion sensor signal, detectinga period of low motion of the apparatus consistent with sleep of a userover a specified time sufficient to permit detection of sleep apnea;detecting, based on the PPG signals, an indication of tachycardia; andin response thereto, marking a portion of the PPG signals correspondingto the period of low motion as representing tachycardia. This approachalso may include marking the portion of the PPG signals as representingsleep apnea.

Additionally or alternatively, the system may be programmed to perform,based upon the motion sensor signal, detecting a period of low motion ofthe apparatus consistent with sleep of a user; detecting, based on thePPG signals, a drop in heart rate during the period of low motionconsistent with sleep; and generating and storing, based on a portion ofthe PPG signals not associated with the period of low motion consistentwith sleep, and the drop in heart rate during the period of low motionconsistent with sleep, an estimate of heart rate variability.

The system may be programmed as part of block 109 to performclassifying, based on the PPG signals, a portion of the PPG signals asrepresenting atrial fibrillation. Additionally, block 109 may includegenerating and storing, in association with the portion of the PPGsignals representing atrial fibrillation, values specifying an amountand duration of atrial fibrillation. Classifying signals at block 109also may be integrated in an approach comprising obtaining the PPGsignals at a first temporal resolution; and in response to detecting,based on the PPG signals, any one or more of cardiac chronotropicincompetence, cardiac arrhythmia, tachycardia, bradycardia, or atrialfibrillation, performing one or more of modifying a temporal resolutionconfiguration parameter of the monitoring apparatus to cause the PPGsignals to be obtained at a second temporal resolution that is higherthan the first temporal resolution; or outputting an alert signal.

FIG. 1F illustrates an example process of acquiring an electrocardiogram(ECG or EKG) measurement in response to detecting an arrhythmia event.In one embodiment, FIG. 1F may serve as an algorithm that may be used asa basis for programming instructions for control program 124. In oneembodiment, a method or algorithm comprises obtaining one or morephotoplethysmography (PPG) signals from one or more PPG sensors of amonitoring apparatus, the PPG signals being generated based uponoptically detecting pulsed variations in blood flow, as seen at block180. At block 182, the process is configured for generating and storing,based upon the PPG signals, a PPG waveform dataset identifying waveformcharacteristics associated with the PPG signals. At block 184, theprocess is configured for detecting, based upon the PPG waveformdataset, one or more cardiac arrhythmia events. In such an embodiment,the cardiac arrhythmia events comprise any one or more of cardiacchronotropic incompetence, tachycardia, bradycardia, atrial fibrillationor other cardiac arrhythmia. At block 186, in response to detecting theone or more cardiac arrhythmia events, the process is configured toprompt the user to provide an ECG measurement. For example, the processmay generate one or more alert signals, messages or notifications asoutput to the activity monitoring device 101 or host computer 110. Theprocess thus notifies the user of the need to provide an ECGmeasurement.

At block 188, the process acquires an ECG measurement from the user,using the activity monitoring device 101. In one embodiment, activitymonitoring device 101 comprises one or more sensors for taking an ECGmeasurement. For example, activity monitoring device 101 may be awearable wrist-based device that includes a first electrical sensorpositioned under the device (e.g., on the side of the device facing theuser's wrist) with a contact point that contacts the wrist of the handthat is wearing activity monitoring device 101. Activity monitoringdevice 101 may further comprise a second electrical sensor, separatefrom the first electrical sensor. For example, the second electricalsensor may be positioned on the top of device and may provide a contactpoint for a finger from the opposing hand of the user. Thus, a user cancontact the first electrical sensor using the wrist of the hand wearingthe activity monitoring device 101, and the user can contact the secondelectrical sensor using a finger of the opposing hand, thereby creatingan electrical circuit that can be used for obtaining a differential ECGsignal. The process thus uses PPG signals to identify an appropriatetime for acquiring an ECG measurement from the user. FIG. 2 is a graph200 of an example cardiac arrhythmia caused by premature atrialcontractions. Arrows 201-204 indicate beats resulting from anomalouscontraction of the atrium before the normal pacemaker signal. In thiscondition, a beat happens earlier than intended. Usually the followingbeat is longer to compensate for this, as the ventricle repolarizes, acondition indicated by an SA reset signal 205 in the dataset.

FIG. 3 illustrates two graphs providing an example of data analysis thatmay be used to detect premature atrial contraction. Graph 302illustrates the amplitude of PPG signals 103 on the vertical axis ascompared to time represented in the horizontal axis. Arrows 304, 306indicate premature atrial contraction. Graph 310 illustrates runningaverage values extracted from the data shown in graph 302. Based on therunning average values, incidents of low amplitude suggesting prematureatrial contraction may be rapidly identified as they are outliers in thedata of graph 310. In either the activity monitoring device 101 or thehost computer 110, rules for detecting premature atrial contractions canbe programmed using the relationship

If RR[n]<0.9×RR_(running) _(_) _(average) AND RR[n+1]>1.1*RR[n], thenRR[n] is a PAC where RR_(running) _(_) _(average) is a running averageof the RR intervals over a specified number of beats, such as the last10 beats.

FIG. 4 illustrates a graph 400 of an RR interval series associated withatrial fibrillation when a person converts from normal sinus rhythm toAF. Embodiments recognize that in AF, the heart rate is typicallyelevated, and also “disorganized”. In an embodiment, activity monitoringdevice 101 or host computer 110 is configured or programmed to detectpeaks of the PPG intervals and to perform pattern analysis to identifyelevated heart rate and disorganized beat activity. In response todetecting both elevated rate and disorganized activity, activitymonitoring device 101 or host computer 110 is configured or programmedto store a record identifying a point at which AF was detected. Therecord may include, for example, data identifying the PPG signal, a tagor type value signifying AF, a timestamp or sequence number indicating aposition within the entire PPG signal dataset at which the AF was found,and a duration of the AF.

2. Example Implementation for Detecting Atrial Fibrillation

2.1 the Root Mean Square of Successive Differences (“RMSSD”) Approach

The data processing system described thus far, using either activitymonitoring device 101 or host computer 110 or both, provides a set of PPinterbeat intervals. One of the most common arrhythmias is atrialfibrillation, whose prevalence increases with age, so that an estimated10% of the 80-89 year age group suffer from this condition. It increasesthe risk of stroke significantly as blood clots can form in the atria,due to the inefficient pumping of the atria in AF. AF can either bechronic (permanent) or intermittent (referred to as paroxysmal AF). Thesystem described herein may be configured or programmed for determiningeither type.

FIG. 5 illustrates data flow and processing steps in one embodiment thatis configured to characterize atrial fibrillation. PP interval dataobtained at block 105, as previously described for FIG. 1A. In anembodiment, the PP interval data defines a set of times at which PPevents occurred. The PP interval data may be divided as seen at block502 into segments or epochs of fixed duration which are long enough tocontain multiple beats, but short enough that short episodes of AF willnot be swamped by the overall record. The particular time duration foran epoch is not critical, but 5 minutes of data is a workable example.

At block 504, values for the following parameters are determined foreach epoch (e.g., 5 minute period of data):

-   -   The Root Mean Square of Successive Differences (RMSSD) (the        square root of the mean of the squares of the successive        differences between adjacent intervals);    -   NN50 (the number of PP intervals that differ by more than 50 ms,        as a percentage of the overall number of beats);    -   The approximate entropy (ApEn) of the series.        Values for these parameters have been found useful to        characterize the increased irregularity of the cardiac rhythm        during atrial fibrillation. Other metrics that could be used are        the HF/LF ratio in the spectral domain, the standard deviation        of the PP intervals, and others.

Values for these parameters are provided to a classifier 506, which haspreviously been trained on known sequences which contain AF and non-AFintervals. The output of the classifier stage may be a label for theepoch, indicating the presence of AF or non-AF, and a probability value508 indicating the likelihood that AF is present in the epoch.Probability values may, in some embodiments, be generated as real numbervalues between 0 and 1.

At block 510, the process determines whether other epochs are present inthe extracted PP interval data, that is, whether other data remains tobe evaluated. Individual epochs represented in the PP interval data canbe non-overlapping or partially overlap. If the test of block 510 istrue, then control may transfer back to block 502 to repeat theforegoing process for another epoch represented in the data.

If no other epochs remain in the data, then control transfers to block512 at which an overall assessment can be made by assessing output frommultiple epochs. In some embodiments, the system may be programmed touse a user probability value from one epoch to influence a combinedoutput for multiple epochs. For example, if the probability value for aparticular epoch is 0.51, but probability values for the surroundingepochs are lower to indicate non-AF, then the final labeling of theparticular epoch may be set to non-AF also as it may represent a randomartifact.

The classifier 506 in various embodiments can be a linear discriminantclassifier, a quadratic discriminant classifier, a random forestclassifier, a decision tree, a support vector machine classifier, or aneural network classifier. The parameters used by the classifier can betrained using PP intervals that have been obtained from subjects withknown AF conditions; the presence of a known AF condition can beconfirmed by simultaneous recording of an ECG on the same subject, plusvisual inspection by an expert electrophysiologist, for example. Usingthis approach, the classifier will recognize periods of AF with a highdegree of confidence.

2.2 Assessment of Correlated Sympathetic or Parasympathetic Activity

In an embodiment, the process described for FIG. 5 may be programmed orconfigured to first determine whether subjects have correlatedsympathetic or parasympathetic activity, as that can change the patternof irregularity shown in the PP sequence. In one embodiment, the systemis configured or programmed to consider periods of the PP interval data.An example period is 60 minutes of data but other periods may be used inother embodiments. The period is divided into smaller segments, such asepochs of 5 minutes. The interval based power spectrum can be taken ofthe interval sequence in these smaller epochs to produce a powerspectrum value S(f) where f is in units of cycles/interval.

As a basis for the power spectrum value, the system is programmed orconfigured to calculate a low-frequency band and a high frequency bandof heart rate variability, for example, by integrating the spectralpower over the range 0.04-0.15 cycles/interval for the LF power, andfrom 0.15-0.4 cycles/interval for the HF power. When a 60-minute periodof data is used, the foregoing process yields 12 HF estimate values and12 LF estimate values. The system is configured or programmed todetermine a correlation coefficient between the sets of estimate values.If a resulting correlation coefficient is high (e.g., >0.8), then the LFand HF are strongly correlated, indicating that the sympathetic andparasympathetic systems of the subject are well coupled.

Thereafter, the period of data can be divided into well-correlatedversus not-well-correlated datasets, and different processing may beapplied to each of the datasets. It has been empirically determined thatthe impact on the spectrum of AF is different for these groups.

FIG. 6 illustrates data flow and processing steps in one embodiment thatis configured to characterize atrial fibrillation in which correlatedsympathetic or parasympathetic activity is considered. As previouslynoted, natural physiological variability occurs in the rhythm dependenton how well coupled the sympathetic and parasympathetic activation is.In FIG. 6, blocks 105, 502 proceed as previously described for FIG. 5.At block 602, a power spectral density estimate value is determined foreach epoch, which allows a power spectral density value of the epoch tobe calculated. A High Frequency (HF) power value is obtained byintegrating the power over 0.15-0.4 cycles/interval at block 604, and isdeemed to reflect parasympathetic activation of the heart. The LF poweris obtained by integrating the power over 0.04-0.15 cycles/interval, andreflects a mixture of sympathetic and parasympathetic activation. Forsome subjects, the sympathetic and parasympathetic activation will behighly correlated; therefore, a correlation is calculated at block 606yielding a correlation coefficient which is tested at block 608 todetermine whether a correlation appears to be present. Depending on thevalue of the correlation coefficient, at block 610, one of a pluralityof different classifier models can be chosen from among those describedabove in connection with FIG. 5.

The classifier 504 of FIG. 5 then can be used as previously described tocharacterize an AF. Alternatively, the processing described herein torecognize abnormal beats can implemented in the classifier 504 of FIG.5; for example, the classifier may be programmed to add in the number ofabnormal beats observed in an minute epoch as a feature.

2.3 PPG Waveform Shape Analysis

Additionally or alternatively, the system may be programmed orconfigured to analyze the shape of the PPG waveform. This approachrecognizes that characterizing AF may be improved by identifyingpremature atrial contractions (“PACs”), which comprise heart beats inwhich the electrical P-wave happens earlier in time than anticipated,due to an activation of the atria by an ectopic focus. Electrocardiogram(ECG) systems recognize PAC by abnormally shaped P waves, followed by anormal QRS complex. Because these atrial contractions are not under thecontrol of the sinus node, they also give rise to a more erratic rhythmthan is normally observed.

In an embodiment, the system is configured or programmed to recognizethe possibility of a PAC occurrence by comparing the shape of the PPGpulses, and recognizing beats which are likely to be associated with apremature atrial contraction. In one embodiment, the comparing andrecognizing comprise maintaining a periodically or continuously updatedtemplate of the expected pulse shape, and flagging any pulses which aresignificantly different (e.g., differing by more than a threshold amountor percentage) in shape, by comparing the current beat to the template.Various changes in the PPG shape could be caused by movement. Therefore,in one embodiment, the motion sensor signal 102 is used to decide if theactivity monitoring device 101 is moving, or is within 5 seconds ofmovement, as one of the markers of PPG pulse shape. Accordingly, if thesystem identifies pulses that are significantly different in shape thenthe template, and if the motion sensor signal 102 indicates that thedevice 101 is moving or has moved within the last 5 seconds (e.g., bycomparing a motion-related metric generated based on motion sensorsignal 102 to a predetermined threshold value), then the given pulseswill not be flagged as a possible PAC occurrence. Alternatively, if thesystem identifies pulses that are significantly different in shape thenthe template, and if the motion sensor signal 102 indicates that thedevice 101 is not moving and/or has not moved within the last 5 seconds(e.g., by comparing a motion-related metric generated based on themotion sensor signal 102 to a predetermined threshold value), then thegiven pulses will be flagged as a possible PAC occurrence.

FIG. 8 illustrates an example dataset collected over approximately 25seconds, in which each heart beat produces a distinctive peak. Asindicated by arrow 804 in graph 802, one beat differs significantly fromits neighbors in amplitude and partially in shape, so it can be flaggedas a potentially unusual beat. By comparing the beat at arrow 804 totemplates of typical PAC beats, the beat at arrow 804 can be assigned aprobability of being a PAC.

2.4 Integration with Mobile Computing and Cloud-Based Devices

The system described herein can be embedded in an ecosystem in which theactivity monitoring device 101 can communicate with a mobile computingdevice using a wireless link, and the mobile computing device canperform further processing of the signal, or transmit the signals to ahost computer such as a central server for analysis. FIG. 7 illustratesa computer system in which an activity monitoring device is integratedwith a mobile computing device. In an embodiment, the activitymonitoring device 101 communicates either raw data, or processed data toa mobile computing device 702, such as a smartphone, over a wirelesslink 703. Link 703 may use a short-range wireless communicationprotocol, such as Bluetooth, or near-field communication (NFC)protocols, WiFi or equivalents.

The mobile computing device 702 also can communicate with the activitymonitoring device 101 to initiate recording data, instruct the device torecord data at a higher sampling rate, give feedback to the user viavibration or audible or visual signals or displays, or to provide aninterface to the user.

In an embodiment, mobile computing device 702 is configured tocommunicate via the cloud network(s) 108 with a host computer 110, whichmay comprise one or more server computers in any physical location orassociated with a shared computing facility such as a virtualized cloudcomputing service. Host computer 110 can run more complex algorithms torecognize arrhythmias, as well as providing archiving and othercommunication functions. For example, a real-time notification systemcan comprise a server-executed algorithm that detects an unusual pattern(such as ventricular arrhythmias or other malignant arrhythmias, or anyother types of arrhythmias or conditions described herein) and alerts awearer of the activity monitoring device 101 or a medical caregiverassociated with the wearer.

In an embodiment, host computer 110 is communicatively coupled to adatabase 704 of prior measurements. Host computer 110 may be programmedor configured to use the database 704 to compare the received signals tonorms for a user subject, or with population norms. Using theseapproaches, specific rules for each user can be defined and stored foruse under program control. An example programmed rule could comprise,generate an alert to a medical caregiver if a heart rate above 120 bpmis observed for more than 60 seconds. The database 704 also can storeexamples of irregular heart beat shapes and templates for use in theprocesses that have been previously described.

3. Characterization of Other Arrhythmias

The system can also be configured to recognize the followingarrhythmias. The system can also decide to record heart rate undercertain conditions, e.g., when the person is asleep or when the personis exercising, as these may be associated with certain phenomena.

BRADYCARDIA, which comprises a slower than normal heart rate. An exampleis less than 60 beats per minute (bpm), but the exact number depends ona user's age, gender and other values. In an embodiment, the system isprogrammed or configured to calculate heart rate by counting the numberof beats in a defined period and to generate an alert or notification ifthe rate is below the defined threshold

ATRIOVENTRICULAR (AV) BLOCK, which occurs when conduction from atria toventricles is impaired. In an embodiment, AV block can be detected byidentifying a long pause between successive PP beats. For example, asequence of [0.8, 0.9, 0.8, 2.0, 0.8], where the numeric values indicateseconds between beats, might indicate an AV block.

VENTRICULAR TACHYCARDIA, which comprises a fast heartbeat caused byincorrect signaling in the ventricles. An example definition is heartrate of greater than 100 bpm, with at least three irregular heartbeatsin a row. In an embodiment, the system may be programmed or configuredto detect the irregular heartbeats based upon impact on the shape of thePPG waveform.

BIGEMINY, which is a particular arrhythmia in which a normal beat isfollowed by an abnormal beat, for example, a premature ventricularcontraction. It can also be described as a continuous alteration of longand short beats. In an embodiment, the system may be programmed orconfigured to recognize bigeminy by calculating the autocorrelation ofthe PP intervals for time shifts of one and two beats, and identifying anon-correlation pattern or correlation pattern.

TRIGEMINY is a three-beat pattern comprising two normal beats followedby an extra beat, or one normal beat followed by two extra beats. In anembodiment, the system may be programmed or configured to recognizetrigeminy by calculating the autocorrelation of the PP intervals fortime shifts of one and two beats in a total of three or more beats, andidentifying a non-correlation pattern or correlation pattern.

HRV ANOMALIES. Successive estimation of PP intervals allows for reliableestimation of Heart Rate Variability (HRV). Long term trends in HRV suchas gradual decrease can be detected and can be used to trigger an alarm,as decreased HRV is correlated with poor cardiovascular outcomes and isa sign of autonomic imbalance.

SLEEP APNEA. A particular type of rhythm pattern is also associated witha condition called sleep apnea, in which the airflow of a person iseither completely or partially obstructed due to collapse of their upperairway; this leads to a reduction in the oxygen level in the blood,which has a distinct effect on heart rate that is capable of observationin a subject. Oxygen desaturation leads to a reduction in heart rate(bradycardia) followed by a rapid increase in rate after recoverybreaths bring air back into the lungs. Thus, for example, if a user'sbase heart rate is 70 bpm, a reduction to 60 bpm could occur in an apneaepisode, followed by a recovery rate of 80 bpm, over a time period of20-30 seconds.

Accordingly, in some embodiments, the activity monitoring device 101 orhost computer 110 is configured or programmed to identify apnea eventsby detecting bradycardia and tachycardia patterns in specific sequencesat timescales that are consistent with apnea events. For example, in oneembodiment, the activity monitoring device 101 or host computer 110 isconfigured or programmed to inspect values of the PPG signal 103 over aspecified time period, such as 20 to 30 seconds, to identify a period ofbradycardia followed immediately by a period of tachycardia. Templatematching may be used for this purpose. In some embodiments, controlprogram 124 may be configured to obtain PPG signals and a motion sensorsignal as previously described and to perform, based upon the motionsensor signal, detecting a period of low motion of the apparatusconsistent with sleep of a user over a specified time sufficient topermit detection of sleep apnea; detecting, based on the PPG signals, anindication of bradycardia followed immediately by a period oftachycardia over a specified time period (such as 20 to 30 seconds); andin response thereto, marking a portion of the PPG signals correspondingto the period of low motion as representing bradycardia, tachycardia,and/or sleep apnea.

In an embodiment, patterns of change in heart rate associated with sleepapnea may be detected using programming or configuration to calculatethe power spectral density estimate of the signal over fixed epochs ofdata, and then calculating the power in the band correspondingapproximately to 15-30 seconds (e.g., 0.03 to 0.06 Hz).

CHRONOTROPIC INCOMPETENCE AND CLINICAL STRESS TEST. In an embodiment,chronotropic incompetence or “CI” or (also referred to as chronotropicinsufficiency) can be observed by measuring the rate of increase ofheart rate in response to physical activity measured with a motionsensor. In one particular embodiment, the motion sensor signal is usedto detect periods of exercise or activity, and to trigger measurement ofchronotropic incompetence, under control of instructions in the controlprogram 124. Further, the control program 124 may be configured orprogrammed to implement a clinical stress test and to automaticallystore data during the correct time periods, with respect to periods ofmotion, that are appropriate for such a test.

A clinical stress test involves detecting (using a motion sensor) a restperiod following exercise period, and then looking for arrhythmias onlyduring that rest period. In contrast, CI involves detecting exercise(with a motion sensor) and then measuring rate of increase of heart rateduring the exercise period itself. For CI, the system is programmed tomeasure heart rate during exercise, and not performing the more in-depthanalysis required for detecting arrhythmias. Thus, existing heart ratealgorithms can be used for measuring heart rate during exercise for CI,and the system does not necessarily need a period of ultralow motion tocapture it. Specifically, in some embodiments, testing for CI mayinvolve simultaneous measurement of exercise and heart rate. Forexample, the motion sensor 122 may be configured to provide an estimateof exercise intensity, and then the system may utilize existing heartrate algorithms to measure heart rate as a function of the exerciseintensity.

In contrast, the clinical stress test involves a more involved processof detecting arrhythmias, which is difficult to perform during periodsof high motion or exercise. For example, detecting arrhythmias involvesmeasuring precise PPG waveform features like time between particularpeaks or amplitude of the waveform etc., which is difficult toaccomplish during periods of high motion or exercise. Thus, for theclinical stress test, the system is programmed or configured to measureparameters soon after exercise, since that is a time of stress withoutmotion.

The time windows over which PPG data is observed to detect the foregoingarrhythmias may vary. For example, for detecting AF, one minute of datamay be sufficient and for tachycardia or other types, a ten-seconddataset may be sufficient.

4. Benefits of Certain Embodiments

Current techniques for assessment of cardiac arrhythmia require thesubject to wear an electrocardiogram device such as a Holter monitor, anevent recorded or even an implanted loop recorder. While such deviceshave high diagnostic accuracy, they are inconvenient for long term use(e.g., over months or years). In sharp contrast, embodiments describedherein can be implemented using activity monitoring apparatus that iscapable of conveniently wearing on the wrist, ear, head or otherlocations and does not require attachment of electrodes to the chest.

Some cardiac arrhythmias such as paroxysmal atrial fibrillation arerelatively benign in themselves, but require long term monitoring asthey raise the overall risk of stroke and other complications if notwell controlled. The embodiments described herein are well suited tolong term wearing and use to analyze the existence of arrhythmia over along period, with the potential to give a patient and their doctor asense of how well controlled their arrhythmia is.

Further, because AF is often intermittent (e.g., occurring for minutesor hours and then ending as normal sinus rhythm resumes), doctors areinterested in the amount and duration of these intermittent bursts(called the AF burden) as it can help guide the treatment path (e.g.,drugs or cardioversion procedures). The embodiments described herein arewell suited to long-term wearing to detect the AF burden of a patientover a period of many hours or days, and are specifically capable ofdetecting AF episodes that other apparatus would have missed. Inparticular, embodiments are capable of detecting an AF episode wheneverit occurs, including AF episodes that would have been missed because thepatient was not wearing a Holter monitor or undergoing an EKG at thetime that the AF episode occurs.

5. Hardware Overview

5.1 Activity Monitoring Device

In this embodiment, activity monitoring device comprises a wrist band inwhich light sources and detectors are mounted on or within an undersideof the activity monitoring device. The activity monitoring device mayinclude a fastening means to attach activity monitoring device to aportion of a user's body and the specific form of the fastening means isnot critical. The fastening means may be a strap that is passed througha receiving portion of the strap and fastened with hook and/or loopfasteners. Other fastening means may include clips, latches,hook-and-loop fasteners such as VELCRO, clasps, ties, and/or adhesives.The fastening means may be located on any side of the activitymonitoring device such that the fastening device does not interfere withmovement or activity.

In an embodiment, the activity monitoring apparatus may comprise aprocessor, memory, user interface, wireless transceiver, one or moreenvironmental sensors, and one or more biometric sensors other than thedetectors. For example, embodiments may be implemented using a wearablemonitor of the type shown in U.S. Pat. No. 8,948,832 of Fitbit, Inc.,San Francisco, Calif., the entire contents of which are herebyincorporated by reference for all purposes as if fully set forth herein.In other words, the wearable monitor of the type shown in U.S. Pat. No.8,948,832 could be modified based upon the additional disclosure hereinto result in a working activity monitoring apparatus capable ofperforming the functions that are described herein. Therefore, thepresent disclosure presumes that the reader knows and understands U.S.Pat. No. 8,948,832, and this disclosure is directed to persons having alevel of skill sufficient to modify or adapt the wearable monitor of thetype shown in U.S. Pat. No. 8,948,832 based upon the additionaldisclosure herein to result in a working activity monitoring apparatuscapable of performing the functions that are described herein.

The activity monitoring device may further comprise a display, which maybe communicatively coupled to other elements through a CPU, otherprocessor or microcontroller coupled to a memory. Display may beprogrammed or configured to display data such as time, heart rate, andarrhythmia events of a user. In other embodiments, the display may beomitted and data detected by the activity monitoring device may betransmitted using a wireless network transceiver via near-fieldcommunication (NFC), BLUETOOTH, WiFi, or other wireless networkingprotocols to a host computer for analysis, display and/or reporting.

5.2 Host Computer or Server Computer

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be hard-wired to perform thetechniques, or may include digital electronic devices such as one ormore application-specific integrated circuits (ASICs) or fieldprogrammable gate arrays (FPGAs) that are persistently programmed toperform the techniques, or may include one or more general purposehardware processors programmed to perform the techniques pursuant toprogram instructions in firmware, memory, other storage, or acombination. Such special-purpose computing devices may also combinecustom hard-wired logic, ASICs, or FPGAs with custom programming toaccomplish the techniques. The special-purpose computing devices may bedesktop computer systems, portable computer systems, handheld devices,networking devices or any other device that incorporates hard-wiredand/or program logic to implement the techniques.

For example, FIG. 9 is a block diagram that illustrates a computersystem 900 upon which an embodiment of the invention may be implemented.Computer system 900 includes a bus 902 or other communication mechanismfor communicating information, and a hardware processor 904 coupled withbus 902 for processing information. Hardware processor 904 may be, forexample, a general purpose microprocessor.

Computer system 900 also includes a main memory 906, such as a randomaccess memory (RAM) or other dynamic storage device, coupled to bus 902for storing information and instructions to be executed by processor904. Main memory 906 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 904. Such instructions, when stored innon-transitory storage media accessible to processor 904, rendercomputer system 900 into a special-purpose machine that is customized toperform the operations specified in the instructions.

Computer system 900 further includes a read only memory (ROM) 908 orother static storage device coupled to bus 902 for storing staticinformation and instructions for processor 904. A storage device 910,such as a magnetic disk, optical disk, or solid-state drive is providedand coupled to bus 902 for storing information and instructions.

Computer system 900 may be coupled via bus 902 to a display 912, such asa cathode ray tube (CRT), for displaying information to a computer user.An input device 914, including alphanumeric and other keys, is coupledto bus 902 for communicating information and command selections toprocessor 904. Another type of user input device is cursor control 916,such as a mouse, a trackball, or cursor direction keys for communicatingdirection information and command selections to processor 904 and forcontrolling cursor movement on display 912. This input device typicallyhas two degrees of freedom in two axes, a first axis (e.g., x) and asecond axis (e.g., y), that allows the device to specify positions in aplane.

Computer system 900 may implement the techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 900 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 900 in response to processor 904 executing one or more sequencesof one or more instructions contained in main memory 906. Suchinstructions may be read into main memory 906 from another storagemedium, such as storage device 910. Execution of the sequences ofinstructions contained in main memory 906 causes processor 904 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical disks, magnetic disks, or solid-state drives, suchas storage device 910. Volatile media includes dynamic memory, such asmain memory 906. Common forms of storage media include, for example, afloppy disk, a flexible disk, hard disk, solid-state drive, magnetictape, or any other magnetic data storage medium, a CD-ROM, any otheroptical data storage medium, any physical medium with patterns of holes,a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip orcartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 902. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 904 for execution. For example,the instructions may initially be carried on a magnetic disk orsolid-state drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 900 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 902. Bus 902 carries the data tomain memory 906, from which processor 904 retrieves and executes theinstructions. The instructions received by main memory 906 mayoptionally be stored on storage device 910 either before or afterexecution by processor 904.

Computer system 900 also includes a communication interface 918 coupledto bus 902. Communication interface 918 provides a two-way datacommunication coupling to a network link 920 that is connected to alocal network 922. For example, communication interface 918 may be anintegrated services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, communicationinterface 918 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN. Wireless links may also beimplemented. In any such implementation, communication interface 918sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.

Network link 920 typically provides data communication through one ormore networks to other data devices. For example, network link 920 mayprovide a connection through local network 922 to a host computer 924 orto data equipment operated by an Internet Service Provider (ISP) 926.ISP 926 in turn provides data communication services through the worldwide packet data communication network now commonly referred to as the“Internet” 928. Local network 922 and Internet 928 both use electrical,electromagnetic or optical signals that carry digital data streams. Thesignals through the various networks and the signals on network link 920and through communication interface 918, which carry the digital data toand from computer system 900, are example forms of transmission media.

Computer system 900 can send messages and receive data, includingprogram code, through the network(s), network link 920 and communicationinterface 918. In the Internet example, a server 930 might transmit arequested code for an application program through Internet 928, ISP 926,local network 922 and communication interface 918.

The received code may be executed by processor 904 as it is received,and/or stored in storage device 910, or other non-volatile storage forlater execution.

6. Extensions and Alternatives

In the foregoing specification, embodiments of the invention have beendescribed with reference to numerous specific details that may vary fromimplementation to implementation. The specification and drawings are,accordingly, to be regarded in an illustrative rather than a restrictivesense.

Various embodiments herein describe a system which monitors the PPGsignal at the wrist using an optical sensor mounted in a wristwatch formfactor, using a technique referred to as reflectancephotoplethysmography. Alternatively, in some embodiments, equivalentinformation is obtained using a finger-tip worn PPG (which usestransmittance photoplethysmography). Alternatively, in otherembodiments, a valid PPG signal is obtained from other parts of the body(e.g., earlobe, forehead etc.) and utilized to determine cardiac rhythm,consistent with the techniques described herein.

Cardiac arrhythmias can also be well detected by measuring theelectrical potential at the surface of the skin (e.g.,electrocardiograms). This is typically done on the chest (to get bestsignal resolution), but can also be done by simply measuring theelectrical potential across the hands. In contrast, the system describedherein utilizes an optical signal instead of an electrical signal todetect arrhythmias and similar issues.

The sole and exclusive indicator of the scope of the invention, and whatis intended by the applicants to be the scope of the invention, is theliteral and equivalent scope of the set of claims that issue from thisapplication, in the specific form in which such claims issue, includingany subsequent correction.

What is claimed is:
 1. A method comprising: obtaining one or morephotoplethysmography (PPG) signals from one or more PPG sensors of amonitoring apparatus, the one or more PPG signals being generated basedupon optically detecting pulsed variations in blood flow; obtaining amotion sensor signal from a motion sensor in the monitoring apparatus;identifying, based upon the motion sensor signal, one or more periods ofmotion of the monitoring apparatus; selectively obtaining and storingsegments of the one or more PPG signals based on a temporal relationshipbetween the segments of the one or more PPG signals and the identifiedone or more periods of motion; determining that at least one segment ofthe one or more PPG signals is indicative of an arrhythmia event; andgenerating, responsive to determining that the at least one segment ofthe one or more PPG signals is indicative of the arrhythmia event, anotification indicating that a user of the monitoring apparatus shouldobtain an electrocardiogram (ECG) measurement.
 2. The method of claim 1,wherein the temporal relationship indicates that the selectivelyobtained and stored segments of the one or more PPG signals do notcorrespond to the identified one or more periods of motion.
 3. Themethod of claim 1, further comprising: generating and storing, basedupon the segments of the one or more PPG signals, PPG waveform dataidentifying waveform characteristics associated with the one or more PPGsignals; and generating and storing, based on the PPG waveform data,arrhythmia event data describing one or more arrhythmia events.
 4. Themethod of claim 3, wherein the PPG waveform data includes a peak-to-peakdataset comprising one or more interval values representing timeintervals between one or more successive peaks in the one or more PPGsignals.
 5. The method of claim 3, wherein the arrhythmia event datadescribes one or more arrhythmia types and arrhythmia durations that areassociated with the one or more arrhythmia types.
 6. The method of claim1, wherein the motion sensor signal comprises one or more signalsselected from the group consisting of: an accelerometer sensor signalfrom an accelerometer, an altitude signal from an altimeter, and agyroscopic motion signal from a gyroscope.
 7. The method of claim 1,further comprising: comparing a motion-related metric generated basedupon the motion sensor signal to a specified motion threshold value;determining, based on the comparing, that the one or more periods ofmotion indicate low motion; and determining and storing one or morearrhythmia types and arrhythmia durations that are associated with theone or more arrhythmia types only during the one or more periods ofmotion that indicate low motion.
 8. The method of claim 1, furthercomprising: comparing a motion-related metric generated based upon themotion sensor signal to a specified motion threshold value; determining,based on the comparing, that the one or more periods of motion indicatesleep; and determining and storing one or more arrhythmia types andarrhythmia durations that are associated with the one or more arrhythmiatypes only during the one or more periods of motion that indicate sleep.9. The method of claim 1, further comprising: comparing a motion-relatedmetric generated based upon the motion sensor signal to a specifiedmotion threshold value; determining, based on the comparing, that theone or more periods of motion indicate high motion; and determining andstoring, responsive to determining that the one or more periods ofmotion indicate high motion, one or more arrhythmia types and arrhythmiadurations that are associated with the one or more arrhythmia types onlyduring additional time periods that do not correspond to the one or moreperiods of motion that indicate high motion.
 10. The method of claim 1,further comprising: comparing a motion-related metric generated basedupon the motion sensor signal to a specified motion threshold value;determining, based upon the comparing, that the one or more periods ofmotion indicate exercise; and determining and storing, responsive todetermining that the one or more periods of motion indicate exercise,one or more arrhythmia types and arrhythmia durations that areassociated with the one or more arrhythmia types only during additionaltime periods that do not correspond to the one or more periods of motionthat indicate exercise.
 11. The method of claim 10, further comprising:obtaining, before the determining that the one or more periods of motionindicate exercise, the one or more PPG signals at a first temporalresolution; determining, based upon the motion sensor signal, that theone or more periods of motion indicating exercise have ended and arefollowed by one or more periods of low motion; and modifying, responsiveto the determining that the one or more periods of motion indicatingexercise have ended and are followed by one or more periods of lowmotion, a temporal resolution configuration parameter of the monitoringapparatus to cause the one or more PPG signals associated with the oneor more periods of low motion to be obtained at a second temporalresolution that is higher than the first temporal resolution.
 12. Themethod of claim 11, further comprising classifying, based on theobtained one or more PPG signals associated with the one or more periodsof low motion, a portion of the one or more PPG signals as representingsuccess or failure in a clinical stress test.
 13. The method of claim10, further comprising: determining, based upon the motion sensorsignal, that the one or more periods of motion indicating exercise haveended and are followed by one or more periods of low motion; anddetermining and storing, responsive to determining that the one or moreperiods of motion indicating exercise have ended and are followed by theone or more periods of low motion, one or more arrhythmia types andarrhythmia durations that are associated with the one or more arrhythmiatypes during the one or more periods of low motion that follow the oneor more periods of motion indicating exercise.
 14. The method of claim1, further comprising: comparing a motion-related metric generated basedupon the motion sensor signal to a specified motion threshold value;determining, based upon the comparing, that the one or more periods ofmotion indicate exercise; and identifying, responsive to determiningthat the one or more periods of motion indicate exercise, an instance ofchronotropic incompetence based on one or more heart rate valuesmeasured during the one or more periods of motion that indicateexercise.
 15. The method of claim 1, further comprising detecting one ormore low amplitude PPG signals in the one or more PPG signals and, inresponse thereto, classifying a portion of the one or more PPG signalscorresponding to the one or more low amplitude PPG signals asrepresenting an arrhythmia.
 16. The method of claim 1, furthercomprising: detecting, based upon the motion sensor signal, a period oflow motion of the apparatus consistent with sleep of a user over aspecified time sufficient to permit detection of sleep apnea; detecting,based on the one or more PPG signals, an indication of bradycardia; andmarking, responsive to detecting the indication of bradycardia, aportion of the one or more PPG signals corresponding to the period oflow motion as representing bradycardia.
 17. The method of claim 15,further comprising marking the portion of the one more PPG signalsrepresenting sleep apnea.
 18. The method of claim 1, further comprising:detecting, based upon the motion sensor signal, a period of low motionof the apparatus consistent with sleep of a user over a specified timesufficient to permit detection of sleep apnea; detecting, based on theone or more PPG signals, an indication of tachycardia; and marking,responsive to detecting the indication of tachycardia, a portion of theone or more PPG signals corresponding to the period of low motion asrepresenting tachycardia.
 19. The method of claim 18, further comprisingmarking the portion of the one or more PPG signals as representing sleepapnea.
 20. The method of claim 1, further comprising: detecting, basedupon the motion sensor signal, a period of low motion of the apparatusconsistent with sleep of a user; detecting, based on the one or more PPGsignals, a drop in heart rate during the period of low motion consistentwith sleep; and generating and storing, based on a) a portion of the oneor more PPG signals not associated with the period of low motionconsistent with sleep and b) the drop in heart rate during the period oflow motion consistent with sleep, an estimate of heart rate variability.21. The method of claim 1, further comprising classifying, based on theone or more PPG signals, a portion of the one or more PPG signals asrepresenting atrial fibrillation.
 22. The method of claim 21, furthercomprising generating and storing, in association with the portion ofthe one or more PPG signals representing atrial fibrillation, valuesspecifying an amount and duration of atrial fibrillation.
 23. The methodof claim 1, further comprising: obtaining the one or more PPG signals ata first temporal resolution; and performing, responsive to detecting,based on the one or more PPG signals, one or more items selected fromthe group consisting of: cardiac chronotropic incompetence, cardiacarrhythmia, tachycardia, bradycardia, and atrial fibrillation,performing at least one action selected from the group consisting of:modifying a temporal resolution configuration parameter of themonitoring apparatus to cause the one or more PPG signals to be obtainedat a second temporal resolution that is higher than the first temporalresolution; and outputting an alert signal.
 24. A system comprising: oneor more processors coupled to electronic digital memory, the electronicdigital memory storing one or more sequences of programmed instructionswhich, when executed by the one or more processors, cause the one ormore processors to: obtain one or more photoplethysmography (PPG)signals from one or more PPG sensors of a monitoring apparatus, the oneor more PPG signals being generated based upon optical detection ofpulsed variations in blood flow by the one or more PPG sensors; obtain amotion sensor signal from a motion sensor in the monitoring apparatus;identify, based upon the motion sensor signal, one or more periods ofmotion of the monitoring apparatus; selectively obtain and storesegments of the one or more PPG signals based on a temporal relationshipbetween the segments of the one or more PPG signals and the identifiedone or more periods of motion; determine that at least one segment ofthe one or more PPG signals is indicative of an arrhythmia event; andgenerate, responsive to determining that the at least one segment of theone or more PPG signals is indicative of the arrhythmia event, anotification indicating that a user of the monitoring apparatus shouldobtain an electrocardiogram (ECG) measurement.
 25. The system of claim24, wherein the one or more sequences of programmed instructions, whenexecuted by the one or more processors, further cause the one or moreprocessors to selectively obtain an store the segments of the one ormore PPG signals based on the temporal relationship indicating that thesegments of the one or more PPG signals do not correspond to theidentified one or more periods of motion.
 26. The system of claim 24,wherein the one or more sequences of programmed instructions, whenexecuted by the one or more processors, further cause the one or moreprocessors to: generate and store PPG waveform data, based upon thesegments of the one or more PPG signals, identifying waveformcharacteristics associated with the one or more PPG signals; andgenerate and store arrhythmia event data, based upon the PPG waveformdata and the one or more periods of motion, describing one or morearrhythmia events.
 27. The system of claim 26, wherein the PPG waveformdata includes a peak-to-peak dataset comprising one or more intervalvalues representing time intervals between one or more successive peaksin the one or more PPG signals.
 28. One or more non-transitorycomputer-readable media storing one or more sequences of instructionswhich, when executed by one or more processors, cause the one or moreprocessors to: obtain one or more photoplethysmography (PPG) signalsfrom one or more PPG sensors of a monitoring apparatus, the one or morePPG signals being generated based upon optically detecting pulsedvariations in blood flow; obtain a motion sensor signal from a motionsensor in the monitoring apparatus; identify, based upon the motionsensor signal, one or more periods of motion of the monitoringapparatus; selectively obtain and store segments of the one or more PPGsignals based on a temporal relationship between the segments of the oneor more PPG signals and the identified one or more periods of motion;determine that at least one segment of the one or more PPG signals isindicative of an arrhythmia event; and generate, responsive todetermining that the at least one segment of the one or more PPG signalsis indicative of the arrhythmia event, a notification indicating that auser of the monitoring apparatus should obtain an electrocardiogram(ECG) measurement.
 29. The system of claim 24, wherein the monitoringapparatus is a wrist-wearable monitoring apparatus that further includesan electrocardiogram sensor having: a first electrode that facestowards, and contacts with, a wearer's wrist when the monitoringapparatus is worn on that wrist, and a second electrode that is separatefrom the first electrode and located on a surface of the monitoringapparatus on a different side of the monitoring apparatus from the firstelectrode, wherein the notification indicating that a user of themonitoring apparatus should obtain an electrocardiogram (ECG)measurement prompts the user to obtain the ECG measurement using theelectrocardiogram sensor of the monitoring apparatus.
 30. The method ofclaim 1, wherein: the notification prompts the user of the monitoringapparatus to obtain the ECG measurement using an electrocardiogramsensor of the monitoring apparatus, the electrocardiogram sensor having:a first electrode that faces towards, and contacts with, a wearer'swrist when the monitoring apparatus is worn on that wrist, and a secondelectrode that is separate from the first electrode and located on asurface of the monitoring apparatus on a different side of themonitoring apparatus from the first electrode, wherein the notificationindicating that a user of the monitoring apparatus should obtain anelectrocardiogram (ECG) measurement prompts the user to obtain the ECGmeasurement using the electrocardiogram sensor of the monitoringapparatus, and the method further comprises acquiring the ECGmeasurement using the electrocardiogram sensor.